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Developing Resilient Modulus Prediction Models Based on Experimental Results of Crushed Hornfels Mixes with Different Gradations and Plasticity
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Publication Date
Mon Jun 19 2023
Journal Name
Journal Of Engineering
An Experimental Analysis of Embankment on Stone Columns
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When embankment is constructed on very soft soil, special construction methods are adopted. One of the techniques is a piled embankment. Piled (stone columns) embankments provide an economic and effective solution to the problem of constructing embankments over soft soils. This method can reduce settlements, construction time and cost. Stone columns provide an effective improvement method for soft soils under light structures such as rail or road embankments. The present work investigates the behavior of the embankment models resting on soft soil reinforced with stone columns. Model tests were performed with different spacing distances between stone columns and two lengths to diameter ratios of the stone columns, in addition to different

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Publication Date
Fri Nov 01 2019
Journal Name
Journal Of Engineering
Prediction of Heat Transfer Coefficient and Pressure Drop in Wire Heat Exchanger Working with R-134a and R-600a
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An experimental and theoretical works were carried out to model the wire condenser in the domestic refrigerator by calculating the heat transfer coefficient and pressure drop and finding the optimum performance. The two methods were used for calculation, zone method, and an integral method. The work was conducted by using two wire condensers with equal length but different in tube diameters, two refrigerants, R-134a and R-600a, and two different compressors matching the refrigerant type. In the experimental work, the optimum charge was found for the refrigerator according to ASHRAE recommendation. Then, the tests were done at 32˚C ambient temperature in a closed room with dimension (2m*2m*3m). The results showed that th

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Publication Date
Mon Jan 01 2024
Journal Name
Aip Conference Proceedings
Non-linear support vector machine classification models using kernel tricks with applications
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The support vector machine, also known as SVM, is a type of supervised learning model that can be used for classification or regression depending on the datasets. SVM is used to classify data points by determining the best hyperplane between two or more groups. Working with enormous datasets, on the other hand, might result in a variety of issues, including inefficient accuracy and time-consuming. SVM was updated in this research by applying some non-linear kernel transformations, which are: linear, polynomial, radial basis, and multi-layer kernels. The non-linear SVM classification model was illustrated and summarized in an algorithm using kernel tricks. The proposed method was examined using three simulation datasets with different sample

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Publication Date
Sat Feb 21 2026
Journal Name
Journal Of Baghdad College Of Dentistry
Effect of different staining materials on color stability of sapphire brackets bonded with different types of light cure orthodontic adhesives (An in vitro study)
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Background: The demand for better esthetic during orthodontic treatment has increased nowadays, so orthodontists starting using esthetic arch wires, brackets and ligatures.Tooth colored brackets were introduced in different types of materials. Sapphire ceramic brackets are one type of esthetic brackets and their color stability remains the main concern for the clinicians and patients at the same time. The present study design to evaluate the effect of three different staining materials (pepsi, black tea and cigarette smoke) on the stainability of sapphire ceramic brackets bonded with three types of light cure orthodontic adhesives which include: Resilience, Enlight and Transbond. Materials and Methods: The sample consisted of three hundre

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Publication Date
Mon Mar 31 2025
Journal Name
International Journal Of Advanced Technology And Engineering Exploration
Breast cancer survival rate prediction using multimodal deep learning with multigenetic features
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Breast cancer is a heterogeneous disease characterized by molecular complexity. This research utilized three genetic expression profiles—gene expression, deoxyribonucleic acid (DNA) methylation, and micro ribonucleic acid (miRNA) expression—to deepen the understanding of breast cancer biology and contribute to the development of a reliable survival rate prediction model. During the preprocessing phase, principal component analysis (PCA) was applied to reduce the dimensionality of each dataset before computing consensus features across the three omics datasets. By integrating these datasets with the consensus features, the model's ability to uncover deep connections within the data was significantly improved. The proposed multimodal deep

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Publication Date
Sun Mar 25 2018
Journal Name
Biomedical And Pharmacology Journal
Apical Extrusion of Debris and Irrigants After using Different Irrigation Needles and Systems With Different Depth of Penetration (A Comparative Study)
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Publication Date
Mon Mar 01 2021
Journal Name
Journal Of Physics: Conference Series
Some Results in Grűnwald-Letnikov Fractional Derivative and its Best Approximation
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Abstract<p>In This paper, we have been approximated Grűnwald-Letnikov Derivative of a function having m continuous derivatives by Bernstein Chlodowsky polynomials with proving its best approximation. As well as we have been solved Bagley-Torvik equation and Fokker–Planck equation where the derivative is in Grűnwald-Letnikov sense.</p>
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Publication Date
Wed Jan 17 2018
Journal Name
Journal Of Engineering And Applied Sciences
PREPARATION, CHARACTERIZATION AND THERMAL ANALYSIS OF POLYMERIC BLEND NANOCOMPOSITES BASED ON PVA-PVPPEGDOPED WITH ZINC OXIDE NANOPARTICLES
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Publication Date
Mon Jan 01 2024
Journal Name
Ieee Access
Patient Monitoring System Based on Internet of Things: A Review and Related Challenges With Open Research Issues
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Publication Date
Wed Aug 07 2024
Journal Name
Civileng
Experimental and FE Investigations of Backfill Cover on Large-Diameter GRP Pipes
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This paper presents experimental investigations on buried Glass Reinforced Plastic (GRP) pipes with a diameter of 1400 mm. The tested pipes were buried in dense, gravelly sand and subjected to traffic loads to study the effects of backfill cover on pipe deflection. The experimental program included tests on three GRP pipes with backfill covers of 100 cm, 75 cm, and 50 cm. The maximum traffic loads applied to the pipe–soil system corresponded to Iraqi Truck Type 3 (AASHTO H type). Vertical deflections of the pipes were monitored during the application of these loads. The experimental results showed that, as the backfill cover increased, the maximum vertical deflection of the pipe decreased. Deflection reductions were 38.0% and 33.3

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